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A Link-Based Cluster Ensemble Approach for Categorical Data Clustering

A Link-Based Cluster Ensemble Approach for Categorical Data Clustering. Presenter : Jian-Ren Chen Authors : Natthakan Iam -On, Tossapon Boongoen , Simon Garrett, and Chris Price 2012 , IEEE. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments.

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A Link-Based Cluster Ensemble Approach for Categorical Data Clustering

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  1. A Link-Based Cluster Ensemble Approach for Categorical Data Clustering Presenter : Jian-Ren ChenAuthors : NatthakanIam-On, TossaponBoongoen,Simon Garrett, and Chris Price 2012 , IEEE

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Cluster Ensembles: combine different clustering decisions in such a way as to achieve accuracy superior to that of any individual clustering.

  4. Objectives • A new link-based approach improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble.

  5. Methodology Creating a Cluster Ensemble Generating a Refined Matrix Applying a Consensus Function to RM

  6. Methodology Creating a Cluster Ensemble Type I (Direct ensemble): Generating a Refined Matrix Applying a Consensus Function to RM Type III (Subspace ensemble) Type II (Full-space ensemble)

  7. Methodology Creating a Cluster Ensemble Generating a Refined Matrix Applying a Consensus Function to RM

  8. Methodology Creating a Cluster Ensemble Generating a Refined Matrix Applying a Consensus Function to RM

  9. Methodology Creating a Cluster Ensemble • given a graph G = (V,W) • SPEC finds the K largest eigenvectors of W • formed another matrix U Generating a Refined Matrix Applying a Consensus Function to RM

  10. Experiments • Investigated Data Sets

  11. Experiments

  12. Experiments

  13. Experiments

  14. Conclusions • Constructing the RM is efficiently resolved by the similarity among categorical labels, using the Weighted Triple-Quality similarity algorithm. • The link-based method usually achieves superior clustering results.

  15. Comments • Advantages • The link-based method is efficient. • Applications • Categorical Data Clustering

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